Overview

Dataset statistics

Number of variables58
Number of observations10000
Missing cells10242
Missing cells (%)1.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.4 MiB
Average record size in memory464.0 B

Variable types

Numeric5
Categorical53

Warnings

country_code has a high cardinality: 94 distinct values High cardinality
region has a high cardinality: 453 distinct values High cardinality
city has a high cardinality: 1746 distinct values High cardinality
country_code has 1121 (11.2%) missing values Missing
region has 1121 (11.2%) missing values Missing
city has 1121 (11.2%) missing values Missing
total_funding_usd has 6879 (68.8%) missing values Missing
total_funding_usd is highly skewed (γ1 = 27.75918257) Skewed
Unnamed: 0 is uniformly distributed Uniform
Unnamed: 0 has unique values Unique

Reproduction

Analysis started2021-02-21 04:34:23.110948
Analysis finished2021-02-21 04:35:10.419094
Duration47.31 seconds
Software versionpandas-profiling v2.10.0
Download configurationconfig.yaml

Variables

Unnamed: 0
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4999.5
Minimum0
Maximum9999
Zeros1
Zeros (%)< 0.1%
Memory size78.2 KiB
2021-02-20T23:35:10.612513image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile499.95
Q12499.75
median4999.5
Q37499.25
95-th percentile9499.05
Maximum9999
Range9999
Interquartile range (IQR)4999.5

Descriptive statistics

Standard deviation2886.89568
Coefficient of variation (CV)0.5774368797
Kurtosis-1.2
Mean4999.5
Median Absolute Deviation (MAD)2500
Skewness0
Sum49995000
Variance8334166.667
MonotocityStrictly increasing
2021-02-20T23:35:10.801148image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
88651
 
< 0.1%
68061
 
< 0.1%
47591
 
< 0.1%
88571
 
< 0.1%
27161
 
< 0.1%
6691
 
< 0.1%
68141
 
< 0.1%
47671
 
< 0.1%
27241
 
< 0.1%
Other values (9990)9990
99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
ValueCountFrequency (%)
99991
< 0.1%
99981
< 0.1%
99971
< 0.1%
99961
< 0.1%
99951
< 0.1%

country_code
Categorical

HIGH CARDINALITY
MISSING

Distinct94
Distinct (%)1.1%
Missing1121
Missing (%)11.2%
Memory size78.2 KiB
USA
5928 
GBR
607 
CAN
 
375
IND
 
240
DEU
 
195
Other values (89)
1534 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters26637
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)0.2%

Sample

1st rowUSA
2nd rowUSA
3rd rowUSA
4th rowUSA
5th rowUSA
ValueCountFrequency (%)
USA5928
59.3%
GBR607
 
6.1%
CAN375
 
3.8%
IND240
 
2.4%
DEU195
 
1.9%
FRA162
 
1.6%
AUS121
 
1.2%
ISR103
 
1.0%
NLD99
 
1.0%
ESP94
 
0.9%
Other values (84)955
 
9.6%
(Missing)1121
 
11.2%
2021-02-20T23:35:11.158946image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
usa5928
66.8%
gbr607
 
6.8%
can375
 
4.2%
ind240
 
2.7%
deu195
 
2.2%
fra162
 
1.8%
aus121
 
1.4%
isr103
 
1.2%
nld99
 
1.1%
esp94
 
1.1%
Other values (84)955
 
10.8%

Most occurring characters

ValueCountFrequency (%)
A6756
25.4%
S6385
24.0%
U6354
23.9%
R1199
 
4.5%
N945
 
3.5%
G721
 
2.7%
B701
 
2.6%
D570
 
2.1%
C529
 
2.0%
I526
 
2.0%
Other values (16)1951
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter26637
100.0%

Most frequent character per category

ValueCountFrequency (%)
A6756
25.4%
S6385
24.0%
U6354
23.9%
R1199
 
4.5%
N945
 
3.5%
G721
 
2.7%
B701
 
2.6%
D570
 
2.1%
C529
 
2.0%
I526
 
2.0%
Other values (16)1951
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
Latin26637
100.0%

Most frequent character per script

ValueCountFrequency (%)
A6756
25.4%
S6385
24.0%
U6354
23.9%
R1199
 
4.5%
N945
 
3.5%
G721
 
2.7%
B701
 
2.6%
D570
 
2.1%
C529
 
2.0%
I526
 
2.0%
Other values (16)1951
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII26637
100.0%

Most frequent character per block

ValueCountFrequency (%)
A6756
25.4%
S6385
24.0%
U6354
23.9%
R1199
 
4.5%
N945
 
3.5%
G721
 
2.7%
B701
 
2.6%
D570
 
2.1%
C529
 
2.0%
I526
 
2.0%
Other values (16)1951
 
7.3%

region
Categorical

HIGH CARDINALITY
MISSING

Distinct453
Distinct (%)5.1%
Missing1121
Missing (%)11.2%
Memory size78.2 KiB
California
2459 
New York
646 
Massachusetts
 
335
England
 
324
Texas
 
302
Other values (448)
4813 

Length

Max length36
Median length10
Mean length9.411194954
Min length3

Characters and Unicode

Total characters83562
Distinct characters55
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique168 ?
Unique (%)1.9%

Sample

1st rowNew York
2nd rowNew York
3rd rowCalifornia
4th rowCalifornia
5th rowCalifornia
ValueCountFrequency (%)
California2459
24.6%
New York646
 
6.5%
Massachusetts335
 
3.4%
England324
 
3.2%
Texas302
 
3.0%
Washington273
 
2.7%
Florida204
 
2.0%
Ontario178
 
1.8%
Illinois171
 
1.7%
Virginia136
 
1.4%
Other values (443)3851
38.5%
(Missing)1121
 
11.2%
2021-02-20T23:35:11.519813image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
california2459
22.8%
new813
 
7.5%
york652
 
6.0%
massachusetts335
 
3.1%
england324
 
3.0%
texas302
 
2.8%
washington273
 
2.5%
florida204
 
1.9%
ontario178
 
1.7%
illinois171
 
1.6%
Other values (516)5066
47.0%

Most occurring characters

ValueCountFrequency (%)
a11109
13.3%
i9012
 
10.8%
n6854
 
8.2%
o6479
 
7.8%
r6145
 
7.4%
l5193
 
6.2%
e4494
 
5.4%
s3770
 
4.5%
C3056
 
3.7%
t2737
 
3.3%
Other values (45)24713
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter70112
83.9%
Uppercase Letter11015
 
13.2%
Space Separator1898
 
2.3%
Dash Punctuation483
 
0.6%
Other Punctuation54
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
a11109
15.8%
i9012
12.9%
n6854
9.8%
o6479
9.2%
r6145
8.8%
l5193
7.4%
e4494
 
6.4%
s3770
 
5.4%
t2737
 
3.9%
f2649
 
3.8%
Other values (16)11670
16.6%
ValueCountFrequency (%)
C3056
27.7%
N1162
 
10.5%
M745
 
6.8%
Y666
 
6.0%
T506
 
4.6%
W496
 
4.5%
I399
 
3.6%
A382
 
3.5%
F378
 
3.4%
O363
 
3.3%
Other values (15)2862
26.0%
ValueCountFrequency (%)
'40
74.1%
,14
 
25.9%
ValueCountFrequency (%)
1898
100.0%
ValueCountFrequency (%)
-483
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin81127
97.1%
Common2435
 
2.9%

Most frequent character per script

ValueCountFrequency (%)
a11109
13.7%
i9012
11.1%
n6854
 
8.4%
o6479
 
8.0%
r6145
 
7.6%
l5193
 
6.4%
e4494
 
5.5%
s3770
 
4.6%
C3056
 
3.8%
t2737
 
3.4%
Other values (41)22278
27.5%
ValueCountFrequency (%)
1898
77.9%
-483
 
19.8%
'40
 
1.6%
,14
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII83562
100.0%

Most frequent character per block

ValueCountFrequency (%)
a11109
13.3%
i9012
 
10.8%
n6854
 
8.2%
o6479
 
7.8%
r6145
 
7.4%
l5193
 
6.2%
e4494
 
5.4%
s3770
 
4.5%
C3056
 
3.7%
t2737
 
3.3%
Other values (45)24713
29.6%

city
Categorical

HIGH CARDINALITY
MISSING

Distinct1746
Distinct (%)19.7%
Missing1121
Missing (%)11.2%
Memory size78.2 KiB
San Francisco
 
678
New York
 
538
London
 
327
Seattle
 
167
Los Angeles
 
166
Other values (1741)
7003 

Length

Max length27
Median length8
Mean length8.759432369
Min length3

Characters and Unicode

Total characters77775
Distinct characters72
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1043 ?
Unique (%)11.7%

Sample

1st rowNew York
2nd rowNew York
3rd rowPalo Alto
4th rowWest Hollywood
5th rowCulver City
ValueCountFrequency (%)
San Francisco678
 
6.8%
New York538
 
5.4%
London327
 
3.3%
Seattle167
 
1.7%
Los Angeles166
 
1.7%
Austin141
 
1.4%
Palo Alto137
 
1.4%
Mountain View114
 
1.1%
Chicago114
 
1.1%
Toronto101
 
1.0%
Other values (1736)6396
64.0%
(Missing)1121
 
11.2%
2021-02-20T23:35:11.910584image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
san1016
 
8.3%
francisco683
 
5.6%
new573
 
4.7%
york542
 
4.4%
london329
 
2.7%
los194
 
1.6%
seattle167
 
1.4%
angeles166
 
1.4%
santa154
 
1.3%
austin141
 
1.2%
Other values (1798)8246
67.5%

Most occurring characters

ValueCountFrequency (%)
a7665
 
9.9%
n7113
 
9.1%
o6777
 
8.7%
e6066
 
7.8%
r4533
 
5.8%
i4270
 
5.5%
l3981
 
5.1%
t3435
 
4.4%
3332
 
4.3%
s3256
 
4.2%
Other values (62)27347
35.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter62155
79.9%
Uppercase Letter12209
 
15.7%
Space Separator3332
 
4.3%
Dash Punctuation53
 
0.1%
Other Punctuation24
 
< 0.1%
Modifier Symbol2
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
a7665
12.3%
n7113
11.4%
o6777
10.9%
e6066
9.8%
r4533
 
7.3%
i4270
 
6.9%
l3981
 
6.4%
t3435
 
5.5%
s3256
 
5.2%
c2358
 
3.8%
Other values (30)12701
20.4%
ValueCountFrequency (%)
S1954
16.0%
C894
 
7.3%
A893
 
7.3%
B885
 
7.2%
M842
 
6.9%
F840
 
6.9%
L840
 
6.9%
N758
 
6.2%
P665
 
5.4%
Y557
 
4.6%
Other values (17)3081
25.2%
ValueCountFrequency (%)
'12
50.0%
.12
50.0%
ValueCountFrequency (%)
3332
100.0%
ValueCountFrequency (%)
-53
100.0%
ValueCountFrequency (%)
`2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin74364
95.6%
Common3411
 
4.4%

Most frequent character per script

ValueCountFrequency (%)
a7665
 
10.3%
n7113
 
9.6%
o6777
 
9.1%
e6066
 
8.2%
r4533
 
6.1%
i4270
 
5.7%
l3981
 
5.4%
t3435
 
4.6%
s3256
 
4.4%
c2358
 
3.2%
Other values (57)24910
33.5%
ValueCountFrequency (%)
3332
97.7%
-53
 
1.6%
'12
 
0.4%
.12
 
0.4%
`2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII77623
99.8%
None152
 
0.2%

Most frequent character per block

ValueCountFrequency (%)
a7665
 
9.9%
n7113
 
9.2%
o6777
 
8.7%
e6066
 
7.8%
r4533
 
5.8%
i4270
 
5.5%
l3981
 
5.1%
t3435
 
4.4%
3332
 
4.3%
s3256
 
4.2%
Other values (47)27195
35.0%
ValueCountFrequency (%)
é53
34.9%
ü26
17.1%
ö15
 
9.9%
Ç13
 
8.6%
ã10
 
6.6%
ó8
 
5.3%
á5
 
3.3%
ä5
 
3.3%
í5
 
3.3%
ø4
 
2.6%
Other values (5)8
 
5.3%

status
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
operating
5850 
acquired
2442 
closed
1595 
ipo
 
113

Length

Max length9
Median length9
Mean length8.2095
Min length3

Characters and Unicode

Total characters82095
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowacquired
2nd rowacquired
3rd rowclosed
4th rowacquired
5th rowacquired
ValueCountFrequency (%)
operating5850
58.5%
acquired2442
24.4%
closed1595
 
16.0%
ipo113
 
1.1%
2021-02-20T23:35:12.297623image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:12.400935image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
operating5850
58.5%
acquired2442
24.4%
closed1595
 
16.0%
ipo113
 
1.1%

Most occurring characters

ValueCountFrequency (%)
e9887
12.0%
i8405
10.2%
a8292
10.1%
r8292
10.1%
o7558
9.2%
p5963
7.3%
t5850
7.1%
n5850
7.1%
g5850
7.1%
c4037
 
4.9%
Other values (5)12111
14.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter82095
100.0%

Most frequent character per category

ValueCountFrequency (%)
e9887
12.0%
i8405
10.2%
a8292
10.1%
r8292
10.1%
o7558
9.2%
p5963
7.3%
t5850
7.1%
n5850
7.1%
g5850
7.1%
c4037
 
4.9%
Other values (5)12111
14.8%

Most occurring scripts

ValueCountFrequency (%)
Latin82095
100.0%

Most frequent character per script

ValueCountFrequency (%)
e9887
12.0%
i8405
10.2%
a8292
10.1%
r8292
10.1%
o7558
9.2%
p5963
7.3%
t5850
7.1%
n5850
7.1%
g5850
7.1%
c4037
 
4.9%
Other values (5)12111
14.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII82095
100.0%

Most frequent character per block

ValueCountFrequency (%)
e9887
12.0%
i8405
10.2%
a8292
10.1%
r8292
10.1%
o7558
9.2%
p5963
7.3%
t5850
7.1%
n5850
7.1%
g5850
7.1%
c4037
 
4.9%
Other values (5)12111
14.8%

total_funding_usd
Real number (ℝ≥0)

MISSING
SKEWED

Distinct1701
Distinct (%)54.5%
Missing6879
Missing (%)68.8%
Infinite0
Infinite (%)0.0%
Mean38577344.28
Minimum1000
Maximum6784000000
Zeros0
Zeros (%)0.0%
Memory size78.2 KiB
2021-02-20T23:35:12.563159image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile130300
Q12000000
median10000000
Q334072000
95-th percentile141000000
Maximum6784000000
Range6783999000
Interquartile range (IQR)32072000

Descriptive statistics

Standard deviation157666618.7
Coefficient of variation (CV)4.08702625
Kurtosis1098.691924
Mean38577344.28
Median Absolute Deviation (MAD)9400000
Skewness27.75918257
Sum1.203998915 × 1011
Variance2.485876267 × 1016
MonotocityNot monotonic
2021-02-20T23:35:12.754797image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000078
 
0.8%
500000050
 
0.5%
200000038
 
0.4%
150000035
 
0.4%
600000034
 
0.3%
25000034
 
0.3%
400000034
 
0.3%
300000032
 
0.3%
50000031
 
0.3%
10000029
 
0.3%
Other values (1691)2726
 
27.3%
(Missing)6879
68.8%
ValueCountFrequency (%)
10001
 
< 0.1%
35001
 
< 0.1%
40002
 
< 0.1%
70001
 
< 0.1%
1000010
0.1%
ValueCountFrequency (%)
67840000001
< 0.1%
22500000001
< 0.1%
17100000001
< 0.1%
16780000001
< 0.1%
14435001261
< 0.1%

founded_on_year
Real number (ℝ≥0)

Distinct93
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1999.9965
Minimum1903
Maximum2020
Zeros0
Zeros (%)0.0%
Memory size78.2 KiB
2021-02-20T23:35:13.010389image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1903
5-th percentile1970
Q11999
median2005
Q32007
95-th percentile2008
Maximum2020
Range117
Interquartile range (IQR)8

Descriptive statistics

Standard deviation12.39904276
Coefficient of variation (CV)0.006199532229
Kurtosis6.302209214
Mean1999.9965
Median Absolute Deviation (MAD)3
Skewness-2.299114566
Sum19999965
Variance153.7362614
MonotocityNot monotonic
2021-02-20T23:35:13.199052image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20071794
17.9%
20081418
14.2%
20061213
12.1%
19701020
10.2%
2005743
7.4%
2004533
 
5.3%
2003417
 
4.2%
2000408
 
4.1%
1999394
 
3.9%
2001331
 
3.3%
Other values (83)1729
17.3%
ValueCountFrequency (%)
19031
< 0.1%
19041
< 0.1%
19051
< 0.1%
19062
< 0.1%
19081
< 0.1%
ValueCountFrequency (%)
20205
0.1%
20194
 
< 0.1%
20187
0.1%
20174
 
< 0.1%
201611
0.1%

founded_on_month
Real number (ℝ≥0)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4303
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Memory size78.2 KiB
2021-02-20T23:35:13.420687image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q36
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.467440587
Coefficient of variation (CV)1.010827212
Kurtosis-0.1833439057
Mean3.4303
Median Absolute Deviation (MAD)0
Skewness1.126479001
Sum34303
Variance12.02314422
MonotocityNot monotonic
2021-02-20T23:35:13.607803image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
15807
58.1%
6466
 
4.7%
9411
 
4.1%
8405
 
4.0%
10402
 
4.0%
3399
 
4.0%
4398
 
4.0%
5392
 
3.9%
2370
 
3.7%
7363
 
3.6%
Other values (2)587
 
5.9%
ValueCountFrequency (%)
15807
58.1%
2370
 
3.7%
3399
 
4.0%
4398
 
4.0%
5392
 
3.9%
ValueCountFrequency (%)
12254
2.5%
11333
3.3%
10402
4.0%
9411
4.1%
8405
4.0%

founded_on_day
Real number (ℝ≥0)

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6168
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Memory size78.2 KiB
2021-02-20T23:35:13.986943image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile21
Maximum31
Range30
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.571261364
Coefficient of variation (CV)1.816871644
Kurtosis5.957165215
Mean3.6168
Median Absolute Deviation (MAD)0
Skewness2.628361167
Sum36168
Variance43.18147591
MonotocityNot monotonic
2021-02-20T23:35:14.197822image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
18128
81.3%
15168
 
1.7%
10124
 
1.2%
588
 
0.9%
484
 
0.8%
279
 
0.8%
374
 
0.7%
2073
 
0.7%
771
 
0.7%
1465
 
0.7%
Other values (21)1046
 
10.5%
ValueCountFrequency (%)
18128
81.3%
279
 
0.8%
374
 
0.7%
484
 
0.8%
588
 
0.9%
ValueCountFrequency (%)
3142
0.4%
3064
0.6%
2943
0.4%
2848
0.5%
2746
0.5%

Other
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0.0
8980 
1.0
1020 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0
ValueCountFrequency (%)
0.08980
89.8%
1.01020
 
10.2%
2021-02-20T23:35:14.601511image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:14.732347image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
0.08980
89.8%
1.01020
 
10.2%

Most occurring characters

ValueCountFrequency (%)
018980
63.3%
.10000
33.3%
11020
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
66.7%
Other Punctuation10000
33.3%

Most frequent character per category

ValueCountFrequency (%)
018980
94.9%
11020
 
5.1%
ValueCountFrequency (%)
.10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

ValueCountFrequency (%)
018980
63.3%
.10000
33.3%
11020
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ValueCountFrequency (%)
018980
63.3%
.10000
33.3%
11020
 
3.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0.0
9758 
1.0
 
242

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.09758
97.6%
1.0242
 
2.4%
2021-02-20T23:35:15.004165image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:15.093209image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
0.09758
97.6%
1.0242
 
2.4%

Most occurring characters

ValueCountFrequency (%)
019758
65.9%
.10000
33.3%
1242
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
66.7%
Other Punctuation10000
33.3%

Most frequent character per category

ValueCountFrequency (%)
019758
98.8%
1242
 
1.2%
ValueCountFrequency (%)
.10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

ValueCountFrequency (%)
019758
65.9%
.10000
33.3%
1242
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ValueCountFrequency (%)
019758
65.9%
.10000
33.3%
1242
 
0.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0.0
8210 
1.0
1790 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.08210
82.1%
1.01790
 
17.9%
2021-02-20T23:35:15.422800image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:15.523837image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
0.08210
82.1%
1.01790
 
17.9%

Most occurring characters

ValueCountFrequency (%)
018210
60.7%
.10000
33.3%
11790
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
66.7%
Other Punctuation10000
33.3%

Most frequent character per category

ValueCountFrequency (%)
018210
91.0%
11790
 
8.9%
ValueCountFrequency (%)
.10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

ValueCountFrequency (%)
018210
60.7%
.10000
33.3%
11790
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ValueCountFrequency (%)
018210
60.7%
.10000
33.3%
11790
 
6.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0.0
6850 
1.0
3150 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row0.0
5th row1.0
ValueCountFrequency (%)
0.06850
68.5%
1.03150
31.5%
2021-02-20T23:35:15.782175image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:15.879739image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
0.06850
68.5%
1.03150
31.5%

Most occurring characters

ValueCountFrequency (%)
016850
56.2%
.10000
33.3%
13150
 
10.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
66.7%
Other Punctuation10000
33.3%

Most frequent character per category

ValueCountFrequency (%)
016850
84.2%
13150
 
15.8%
ValueCountFrequency (%)
.10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

ValueCountFrequency (%)
016850
56.2%
.10000
33.3%
13150
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ValueCountFrequency (%)
016850
56.2%
.10000
33.3%
13150
 
10.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0.0
8014 
1.0
1986 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.08014
80.1%
1.01986
 
19.9%
2021-02-20T23:35:16.147256image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:16.238937image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
0.08014
80.1%
1.01986
 
19.9%

Most occurring characters

ValueCountFrequency (%)
018014
60.0%
.10000
33.3%
11986
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
66.7%
Other Punctuation10000
33.3%

Most frequent character per category

ValueCountFrequency (%)
018014
90.1%
11986
 
9.9%
ValueCountFrequency (%)
.10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

ValueCountFrequency (%)
018014
60.0%
.10000
33.3%
11986
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ValueCountFrequency (%)
018014
60.0%
.10000
33.3%
11986
 
6.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0.0
8772 
1.0
1228 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0
ValueCountFrequency (%)
0.08772
87.7%
1.01228
 
12.3%
2021-02-20T23:35:16.564578image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:16.651918image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
0.08772
87.7%
1.01228
 
12.3%

Most occurring characters

ValueCountFrequency (%)
018772
62.6%
.10000
33.3%
11228
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
66.7%
Other Punctuation10000
33.3%

Most frequent character per category

ValueCountFrequency (%)
018772
93.9%
11228
 
6.1%
ValueCountFrequency (%)
.10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

ValueCountFrequency (%)
018772
62.6%
.10000
33.3%
11228
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ValueCountFrequency (%)
018772
62.6%
.10000
33.3%
11228
 
4.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0.0
9963 
1.0
 
37

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.09963
99.6%
1.037
 
0.4%
2021-02-20T23:35:16.882090image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:16.975997image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
0.09963
99.6%
1.037
 
0.4%

Most occurring characters

ValueCountFrequency (%)
019963
66.5%
.10000
33.3%
137
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
66.7%
Other Punctuation10000
33.3%

Most frequent character per category

ValueCountFrequency (%)
019963
99.8%
137
 
0.2%
ValueCountFrequency (%)
.10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

ValueCountFrequency (%)
019963
66.5%
.10000
33.3%
137
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ValueCountFrequency (%)
019963
66.5%
.10000
33.3%
137
 
0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0.0
9218 
1.0
 
782

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.09218
92.2%
1.0782
 
7.8%
2021-02-20T23:35:17.201501image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:17.288556image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
0.09218
92.2%
1.0782
 
7.8%

Most occurring characters

ValueCountFrequency (%)
019218
64.1%
.10000
33.3%
1782
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
66.7%
Other Punctuation10000
33.3%

Most frequent character per category

ValueCountFrequency (%)
019218
96.1%
1782
 
3.9%
ValueCountFrequency (%)
.10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

ValueCountFrequency (%)
019218
64.1%
.10000
33.3%
1782
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ValueCountFrequency (%)
019218
64.1%
.10000
33.3%
1782
 
2.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0.0
9870 
1.0
 
130

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.09870
98.7%
1.0130
 
1.3%
2021-02-20T23:35:17.525215image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:17.615587image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
0.09870
98.7%
1.0130
 
1.3%

Most occurring characters

ValueCountFrequency (%)
019870
66.2%
.10000
33.3%
1130
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
66.7%
Other Punctuation10000
33.3%

Most frequent character per category

ValueCountFrequency (%)
019870
99.4%
1130
 
0.7%
ValueCountFrequency (%)
.10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

ValueCountFrequency (%)
019870
66.2%
.10000
33.3%
1130
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ValueCountFrequency (%)
019870
66.2%
.10000
33.3%
1130
 
0.4%

Design
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0.0
9416 
1.0
 
584

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.09416
94.2%
1.0584
 
5.8%
2021-02-20T23:35:17.887035image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:17.974520image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
0.09416
94.2%
1.0584
 
5.8%

Most occurring characters

ValueCountFrequency (%)
019416
64.7%
.10000
33.3%
1584
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
66.7%
Other Punctuation10000
33.3%

Most frequent character per category

ValueCountFrequency (%)
019416
97.1%
1584
 
2.9%
ValueCountFrequency (%)
.10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

ValueCountFrequency (%)
019416
64.7%
.10000
33.3%
1584
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ValueCountFrequency (%)
019416
64.7%
.10000
33.3%
1584
 
1.9%

Consumer Goods
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0.0
9926 
1.0
 
74

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.09926
99.3%
1.074
 
0.7%
2021-02-20T23:35:18.206015image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:18.289970image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
0.09926
99.3%
1.074
 
0.7%

Most occurring characters

ValueCountFrequency (%)
019926
66.4%
.10000
33.3%
174
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
66.7%
Other Punctuation10000
33.3%

Most frequent character per category

ValueCountFrequency (%)
019926
99.6%
174
 
0.4%
ValueCountFrequency (%)
.10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

ValueCountFrequency (%)
019926
66.4%
.10000
33.3%
174
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ValueCountFrequency (%)
019926
66.4%
.10000
33.3%
174
 
0.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0.0
9366 
1.0
 
634

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0
ValueCountFrequency (%)
0.09366
93.7%
1.0634
 
6.3%
2021-02-20T23:35:18.519600image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:18.614370image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
0.09366
93.7%
1.0634
 
6.3%

Most occurring characters

ValueCountFrequency (%)
019366
64.6%
.10000
33.3%
1634
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
66.7%
Other Punctuation10000
33.3%

Most frequent character per category

ValueCountFrequency (%)
019366
96.8%
1634
 
3.2%
ValueCountFrequency (%)
.10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

ValueCountFrequency (%)
019366
64.6%
.10000
33.3%
1634
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ValueCountFrequency (%)
019366
64.6%
.10000
33.3%
1634
 
2.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0.0
9754 
1.0
 
246

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.09754
97.5%
1.0246
 
2.5%
2021-02-20T23:35:18.842714image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:18.927942image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
0.09754
97.5%
1.0246
 
2.5%

Most occurring characters

ValueCountFrequency (%)
019754
65.8%
.10000
33.3%
1246
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
66.7%
Other Punctuation10000
33.3%

Most frequent character per category

ValueCountFrequency (%)
019754
98.8%
1246
 
1.2%
ValueCountFrequency (%)
.10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

ValueCountFrequency (%)
019754
65.8%
.10000
33.3%
1246
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ValueCountFrequency (%)
019754
65.8%
.10000
33.3%
1246
 
0.8%

Transportation
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0.0
9817 
1.0
 
183

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.09817
98.2%
1.0183
 
1.8%
2021-02-20T23:35:19.169611image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:19.253990image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
0.09817
98.2%
1.0183
 
1.8%

Most occurring characters

ValueCountFrequency (%)
019817
66.1%
.10000
33.3%
1183
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
66.7%
Other Punctuation10000
33.3%

Most frequent character per category

ValueCountFrequency (%)
019817
99.1%
1183
 
0.9%
ValueCountFrequency (%)
.10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

ValueCountFrequency (%)
019817
66.1%
.10000
33.3%
1183
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ValueCountFrequency (%)
019817
66.1%
.10000
33.3%
1183
 
0.6%

Payments
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0.0
9854 
1.0
 
146

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.09854
98.5%
1.0146
 
1.5%
2021-02-20T23:35:19.705214image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:19.789575image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
0.09854
98.5%
1.0146
 
1.5%

Most occurring characters

ValueCountFrequency (%)
019854
66.2%
.10000
33.3%
1146
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
66.7%
Other Punctuation10000
33.3%

Most frequent character per category

ValueCountFrequency (%)
019854
99.3%
1146
 
0.7%
ValueCountFrequency (%)
.10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

ValueCountFrequency (%)
019854
66.2%
.10000
33.3%
1146
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ValueCountFrequency (%)
019854
66.2%
.10000
33.3%
1146
 
0.5%

Sustainability
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0.0
9901 
1.0
 
99

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.09901
99.0%
1.099
 
1.0%
2021-02-20T23:35:20.019900image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:20.106268image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
0.09901
99.0%
1.099
 
1.0%

Most occurring characters

ValueCountFrequency (%)
019901
66.3%
.10000
33.3%
199
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
66.7%
Other Punctuation10000
33.3%

Most frequent character per category

ValueCountFrequency (%)
019901
99.5%
199
 
0.5%
ValueCountFrequency (%)
.10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

ValueCountFrequency (%)
019901
66.3%
.10000
33.3%
199
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ValueCountFrequency (%)
019901
66.3%
.10000
33.3%
199
 
0.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0.0
9023 
1.0
977 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.09023
90.2%
1.0977
 
9.8%
2021-02-20T23:35:20.356721image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:20.444359image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
0.09023
90.2%
1.0977
 
9.8%

Most occurring characters

ValueCountFrequency (%)
019023
63.4%
.10000
33.3%
1977
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
66.7%
Other Punctuation10000
33.3%

Most frequent character per category

ValueCountFrequency (%)
019023
95.1%
1977
 
4.9%
ValueCountFrequency (%)
.10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

ValueCountFrequency (%)
019023
63.4%
.10000
33.3%
1977
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ValueCountFrequency (%)
019023
63.4%
.10000
33.3%
1977
 
3.3%

Energy
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0.0
9928 
1.0
 
72

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.09928
99.3%
1.072
 
0.7%
2021-02-20T23:35:20.687518image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:20.770106image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
0.09928
99.3%
1.072
 
0.7%

Most occurring characters

ValueCountFrequency (%)
019928
66.4%
.10000
33.3%
172
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
66.7%
Other Punctuation10000
33.3%

Most frequent character per category

ValueCountFrequency (%)
019928
99.6%
172
 
0.4%
ValueCountFrequency (%)
.10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

ValueCountFrequency (%)
019928
66.4%
.10000
33.3%
172
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ValueCountFrequency (%)
019928
66.4%
.10000
33.3%
172
 
0.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0.0
9483 
1.0
 
517

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.09483
94.8%
1.0517
 
5.2%
2021-02-20T23:35:20.993386image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:21.121101image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
0.09483
94.8%
1.0517
 
5.2%

Most occurring characters

ValueCountFrequency (%)
019483
64.9%
.10000
33.3%
1517
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
66.7%
Other Punctuation10000
33.3%

Most frequent character per category

ValueCountFrequency (%)
019483
97.4%
1517
 
2.6%
ValueCountFrequency (%)
.10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

ValueCountFrequency (%)
019483
64.9%
.10000
33.3%
1517
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ValueCountFrequency (%)
019483
64.9%
.10000
33.3%
1517
 
1.7%

Video
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0.0
9095 
1.0
 
905

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0
ValueCountFrequency (%)
0.09095
91.0%
1.0905
 
9.0%
2021-02-20T23:35:21.395958image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:21.488324image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
0.09095
91.0%
1.0905
 
9.0%

Most occurring characters

ValueCountFrequency (%)
019095
63.6%
.10000
33.3%
1905
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
66.7%
Other Punctuation10000
33.3%

Most frequent character per category

ValueCountFrequency (%)
019095
95.5%
1905
 
4.5%
ValueCountFrequency (%)
.10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

ValueCountFrequency (%)
019095
63.6%
.10000
33.3%
1905
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ValueCountFrequency (%)
019095
63.6%
.10000
33.3%
1905
 
3.0%

Advertising
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0.0
8838 
1.0
1162 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.08838
88.4%
1.01162
 
11.6%
2021-02-20T23:35:21.813805image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:21.920922image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
0.08838
88.4%
1.01162
 
11.6%

Most occurring characters

ValueCountFrequency (%)
018838
62.8%
.10000
33.3%
11162
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
66.7%
Other Punctuation10000
33.3%

Most frequent character per category

ValueCountFrequency (%)
018838
94.2%
11162
 
5.8%
ValueCountFrequency (%)
.10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

ValueCountFrequency (%)
018838
62.8%
.10000
33.3%
11162
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ValueCountFrequency (%)
018838
62.8%
.10000
33.3%
11162
 
3.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0.0
9850 
1.0
 
150

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.09850
98.5%
1.0150
 
1.5%
2021-02-20T23:35:22.175453image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:22.267344image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
0.09850
98.5%
1.0150
 
1.5%

Most occurring characters

ValueCountFrequency (%)
019850
66.2%
.10000
33.3%
1150
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
66.7%
Other Punctuation10000
33.3%

Most frequent character per category

ValueCountFrequency (%)
019850
99.2%
1150
 
0.8%
ValueCountFrequency (%)
.10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

ValueCountFrequency (%)
019850
66.2%
.10000
33.3%
1150
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ValueCountFrequency (%)
019850
66.2%
.10000
33.3%
1150
 
0.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0.0
9435 
1.0
 
565

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.09435
94.3%
1.0565
 
5.7%
2021-02-20T23:35:22.508941image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:22.631533image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
0.09435
94.3%
1.0565
 
5.7%

Most occurring characters

ValueCountFrequency (%)
019435
64.8%
.10000
33.3%
1565
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
66.7%
Other Punctuation10000
33.3%

Most frequent character per category

ValueCountFrequency (%)
019435
97.2%
1565
 
2.8%
ValueCountFrequency (%)
.10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

ValueCountFrequency (%)
019435
64.8%
.10000
33.3%
1565
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ValueCountFrequency (%)
019435
64.8%
.10000
33.3%
1565
 
1.9%

Music and Audio
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0.0
9490 
1.0
 
510

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.09490
94.9%
1.0510
 
5.1%
2021-02-20T23:35:22.961660image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:23.087820image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
0.09490
94.9%
1.0510
 
5.1%

Most occurring characters

ValueCountFrequency (%)
019490
65.0%
.10000
33.3%
1510
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
66.7%
Other Punctuation10000
33.3%

Most frequent character per category

ValueCountFrequency (%)
019490
97.5%
1510
 
2.5%
ValueCountFrequency (%)
.10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

ValueCountFrequency (%)
019490
65.0%
.10000
33.3%
1510
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ValueCountFrequency (%)
019490
65.0%
.10000
33.3%
1510
 
1.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0.0
9832 
1.0
 
168

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.09832
98.3%
1.0168
 
1.7%
2021-02-20T23:35:23.349781image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:23.465347image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
0.09832
98.3%
1.0168
 
1.7%

Most occurring characters

ValueCountFrequency (%)
019832
66.1%
.10000
33.3%
1168
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
66.7%
Other Punctuation10000
33.3%

Most frequent character per category

ValueCountFrequency (%)
019832
99.2%
1168
 
0.8%
ValueCountFrequency (%)
.10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

ValueCountFrequency (%)
019832
66.1%
.10000
33.3%
1168
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ValueCountFrequency (%)
019832
66.1%
.10000
33.3%
1168
 
0.6%

Hardware
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0.0
8958 
1.0
1042 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.08958
89.6%
1.01042
 
10.4%
2021-02-20T23:35:23.805963image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:23.934988image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
0.08958
89.6%
1.01042
 
10.4%

Most occurring characters

ValueCountFrequency (%)
018958
63.2%
.10000
33.3%
11042
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
66.7%
Other Punctuation10000
33.3%

Most frequent character per category

ValueCountFrequency (%)
018958
94.8%
11042
 
5.2%
ValueCountFrequency (%)
.10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

ValueCountFrequency (%)
018958
63.2%
.10000
33.3%
11042
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ValueCountFrequency (%)
018958
63.2%
.10000
33.3%
11042
 
3.5%

Apps
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0.0
9395 
1.0
 
605

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.09395
94.0%
1.0605
 
6.0%
2021-02-20T23:35:24.185888image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:24.283048image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
0.09395
94.0%
1.0605
 
6.0%

Most occurring characters

ValueCountFrequency (%)
019395
64.6%
.10000
33.3%
1605
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
66.7%
Other Punctuation10000
33.3%

Most frequent character per category

ValueCountFrequency (%)
019395
97.0%
1605
 
3.0%
ValueCountFrequency (%)
.10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

ValueCountFrequency (%)
019395
64.6%
.10000
33.3%
1605
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ValueCountFrequency (%)
019395
64.6%
.10000
33.3%
1605
 
2.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0.0
9724 
1.0
 
276

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.09724
97.2%
1.0276
 
2.8%
2021-02-20T23:35:24.586846image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:24.695062image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
0.09724
97.2%
1.0276
 
2.8%

Most occurring characters

ValueCountFrequency (%)
019724
65.7%
.10000
33.3%
1276
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
66.7%
Other Punctuation10000
33.3%

Most frequent character per category

ValueCountFrequency (%)
019724
98.6%
1276
 
1.4%
ValueCountFrequency (%)
.10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

ValueCountFrequency (%)
019724
65.7%
.10000
33.3%
1276
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ValueCountFrequency (%)
019724
65.7%
.10000
33.3%
1276
 
0.9%

Biotechnology
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0.0
9945 
1.0
 
55

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.09945
99.5%
1.055
 
0.5%
2021-02-20T23:35:25.042404image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:25.160854image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
0.09945
99.5%
1.055
 
0.5%

Most occurring characters

ValueCountFrequency (%)
019945
66.5%
.10000
33.3%
155
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
66.7%
Other Punctuation10000
33.3%

Most frequent character per category

ValueCountFrequency (%)
019945
99.7%
155
 
0.3%
ValueCountFrequency (%)
.10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

ValueCountFrequency (%)
019945
66.5%
.10000
33.3%
155
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ValueCountFrequency (%)
019945
66.5%
.10000
33.3%
155
 
0.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0.0
8768 
1.0
1232 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.08768
87.7%
1.01232
 
12.3%
2021-02-20T23:35:25.535453image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:25.633935image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
0.08768
87.7%
1.01232
 
12.3%

Most occurring characters

ValueCountFrequency (%)
018768
62.6%
.10000
33.3%
11232
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
66.7%
Other Punctuation10000
33.3%

Most frequent character per category

ValueCountFrequency (%)
018768
93.8%
11232
 
6.2%
ValueCountFrequency (%)
.10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

ValueCountFrequency (%)
018768
62.6%
.10000
33.3%
11232
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ValueCountFrequency (%)
018768
62.6%
.10000
33.3%
11232
 
4.1%

Real Estate
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0.0
9747 
1.0
 
253

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.09747
97.5%
1.0253
 
2.5%
2021-02-20T23:35:25.983031image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:26.096046image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
0.09747
97.5%
1.0253
 
2.5%

Most occurring characters

ValueCountFrequency (%)
019747
65.8%
.10000
33.3%
1253
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
66.7%
Other Punctuation10000
33.3%

Most frequent character per category

ValueCountFrequency (%)
019747
98.7%
1253
 
1.3%
ValueCountFrequency (%)
.10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

ValueCountFrequency (%)
019747
65.8%
.10000
33.3%
1253
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ValueCountFrequency (%)
019747
65.8%
.10000
33.3%
1253
 
0.8%

Platforms
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0.0
9676 
1.0
 
324

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.09676
96.8%
1.0324
 
3.2%
2021-02-20T23:35:26.383519image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:26.502024image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
0.09676
96.8%
1.0324
 
3.2%

Most occurring characters

ValueCountFrequency (%)
019676
65.6%
.10000
33.3%
1324
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
66.7%
Other Punctuation10000
33.3%

Most frequent character per category

ValueCountFrequency (%)
019676
98.4%
1324
 
1.6%
ValueCountFrequency (%)
.10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

ValueCountFrequency (%)
019676
65.6%
.10000
33.3%
1324
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ValueCountFrequency (%)
019676
65.6%
.10000
33.3%
1324
 
1.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0.0
9636 
1.0
 
364

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.09636
96.4%
1.0364
 
3.6%
2021-02-20T23:35:26.868067image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:26.992640image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
0.09636
96.4%
1.0364
 
3.6%

Most occurring characters

ValueCountFrequency (%)
019636
65.5%
.10000
33.3%
1364
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
66.7%
Other Punctuation10000
33.3%

Most frequent character per category

ValueCountFrequency (%)
019636
98.2%
1364
 
1.8%
ValueCountFrequency (%)
.10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

ValueCountFrequency (%)
019636
65.5%
.10000
33.3%
1364
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ValueCountFrequency (%)
019636
65.5%
.10000
33.3%
1364
 
1.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0.0
5844 
1.0
4156 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.05844
58.4%
1.04156
41.6%
2021-02-20T23:35:27.378264image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:27.736290image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
0.05844
58.4%
1.04156
41.6%

Most occurring characters

ValueCountFrequency (%)
015844
52.8%
.10000
33.3%
14156
 
13.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
66.7%
Other Punctuation10000
33.3%

Most frequent character per category

ValueCountFrequency (%)
015844
79.2%
14156
 
20.8%
ValueCountFrequency (%)
.10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

ValueCountFrequency (%)
015844
52.8%
.10000
33.3%
14156
 
13.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ValueCountFrequency (%)
015844
52.8%
.10000
33.3%
14156
 
13.9%

Software
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0.0
5590 
1.0
4410 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0
ValueCountFrequency (%)
0.05590
55.9%
1.04410
44.1%
2021-02-20T23:35:28.013427image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:28.125107image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
0.05590
55.9%
1.04410
44.1%

Most occurring characters

ValueCountFrequency (%)
015590
52.0%
.10000
33.3%
14410
 
14.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
66.7%
Other Punctuation10000
33.3%

Most frequent character per category

ValueCountFrequency (%)
015590
78.0%
14410
 
22.1%
ValueCountFrequency (%)
.10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

ValueCountFrequency (%)
015590
52.0%
.10000
33.3%
14410
 
14.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ValueCountFrequency (%)
015590
52.0%
.10000
33.3%
14410
 
14.7%

Health Care
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0.0
9735 
1.0
 
265

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.09735
97.4%
1.0265
 
2.6%
2021-02-20T23:35:28.526477image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:28.730741image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
0.09735
97.4%
1.0265
 
2.6%

Most occurring characters

ValueCountFrequency (%)
019735
65.8%
.10000
33.3%
1265
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
66.7%
Other Punctuation10000
33.3%

Most frequent character per category

ValueCountFrequency (%)
019735
98.7%
1265
 
1.3%
ValueCountFrequency (%)
.10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

ValueCountFrequency (%)
019735
65.8%
.10000
33.3%
1265
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ValueCountFrequency (%)
019735
65.8%
.10000
33.3%
1265
 
0.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0.0
9886 
1.0
 
114

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.09886
98.9%
1.0114
 
1.1%
2021-02-20T23:35:29.032947image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:29.121975image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
0.09886
98.9%
1.0114
 
1.1%

Most occurring characters

ValueCountFrequency (%)
019886
66.3%
.10000
33.3%
1114
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
66.7%
Other Punctuation10000
33.3%

Most frequent character per category

ValueCountFrequency (%)
019886
99.4%
1114
 
0.6%
ValueCountFrequency (%)
.10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

ValueCountFrequency (%)
019886
66.3%
.10000
33.3%
1114
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ValueCountFrequency (%)
019886
66.3%
.10000
33.3%
1114
 
0.4%

Education
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0.0
9576 
1.0
 
424

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.09576
95.8%
1.0424
 
4.2%
2021-02-20T23:35:29.358524image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:29.448081image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
0.09576
95.8%
1.0424
 
4.2%

Most occurring characters

ValueCountFrequency (%)
019576
65.3%
.10000
33.3%
1424
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
66.7%
Other Punctuation10000
33.3%

Most frequent character per category

ValueCountFrequency (%)
019576
97.9%
1424
 
2.1%
ValueCountFrequency (%)
.10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

ValueCountFrequency (%)
019576
65.3%
.10000
33.3%
1424
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ValueCountFrequency (%)
019576
65.3%
.10000
33.3%
1424
 
1.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0.0
9916 
1.0
 
84

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.09916
99.2%
1.084
 
0.8%
2021-02-20T23:35:29.681531image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:29.766381image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
0.09916
99.2%
1.084
 
0.8%

Most occurring characters

ValueCountFrequency (%)
019916
66.4%
.10000
33.3%
184
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
66.7%
Other Punctuation10000
33.3%

Most frequent character per category

ValueCountFrequency (%)
019916
99.6%
184
 
0.4%
ValueCountFrequency (%)
.10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

ValueCountFrequency (%)
019916
66.4%
.10000
33.3%
184
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ValueCountFrequency (%)
019916
66.4%
.10000
33.3%
184
 
0.3%

Sports
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0.0
9770 
1.0
 
230

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.09770
97.7%
1.0230
 
2.3%
2021-02-20T23:35:30.003203image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:30.089530image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
0.09770
97.7%
1.0230
 
2.3%

Most occurring characters

ValueCountFrequency (%)
019770
65.9%
.10000
33.3%
1230
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
66.7%
Other Punctuation10000
33.3%

Most frequent character per category

ValueCountFrequency (%)
019770
98.9%
1230
 
1.1%
ValueCountFrequency (%)
.10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

ValueCountFrequency (%)
019770
65.9%
.10000
33.3%
1230
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ValueCountFrequency (%)
019770
65.9%
.10000
33.3%
1230
 
0.8%

Mobile
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0.0
8680 
1.0
1320 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0
ValueCountFrequency (%)
0.08680
86.8%
1.01320
 
13.2%
2021-02-20T23:35:30.413560image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:30.510851image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
0.08680
86.8%
1.01320
 
13.2%

Most occurring characters

ValueCountFrequency (%)
018680
62.3%
.10000
33.3%
11320
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
66.7%
Other Punctuation10000
33.3%

Most frequent character per category

ValueCountFrequency (%)
018680
93.4%
11320
 
6.6%
ValueCountFrequency (%)
.10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

ValueCountFrequency (%)
018680
62.3%
.10000
33.3%
11320
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ValueCountFrequency (%)
018680
62.3%
.10000
33.3%
11320
 
4.4%

Manufacturing
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0.0
9842 
1.0
 
158

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.09842
98.4%
1.0158
 
1.6%
2021-02-20T23:35:30.758282image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:30.844387image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
0.09842
98.4%
1.0158
 
1.6%

Most occurring characters

ValueCountFrequency (%)
019842
66.1%
.10000
33.3%
1158
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
66.7%
Other Punctuation10000
33.3%

Most frequent character per category

ValueCountFrequency (%)
019842
99.2%
1158
 
0.8%
ValueCountFrequency (%)
.10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

ValueCountFrequency (%)
019842
66.1%
.10000
33.3%
1158
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ValueCountFrequency (%)
019842
66.1%
.10000
33.3%
1158
 
0.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0.0
9899 
1.0
 
101

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.09899
99.0%
1.0101
 
1.0%
2021-02-20T23:35:31.084211image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:31.169101image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
0.09899
99.0%
1.0101
 
1.0%

Most occurring characters

ValueCountFrequency (%)
019899
66.3%
.10000
33.3%
1101
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
66.7%
Other Punctuation10000
33.3%

Most frequent character per category

ValueCountFrequency (%)
019899
99.5%
1101
 
0.5%
ValueCountFrequency (%)
.10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

ValueCountFrequency (%)
019899
66.3%
.10000
33.3%
1101
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ValueCountFrequency (%)
019899
66.3%
.10000
33.3%
1101
 
0.3%

Events
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0.0
9749 
1.0
 
251

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.09749
97.5%
1.0251
 
2.5%
2021-02-20T23:35:31.467503image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:31.556959image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
0.09749
97.5%
1.0251
 
2.5%

Most occurring characters

ValueCountFrequency (%)
019749
65.8%
.10000
33.3%
1251
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
66.7%
Other Punctuation10000
33.3%

Most frequent character per category

ValueCountFrequency (%)
019749
98.7%
1251
 
1.3%
ValueCountFrequency (%)
.10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

ValueCountFrequency (%)
019749
65.8%
.10000
33.3%
1251
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ValueCountFrequency (%)
019749
65.8%
.10000
33.3%
1251
 
0.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0.0
9988 
1.0
 
12

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.09988
99.9%
1.012
 
0.1%
2021-02-20T23:35:31.883449image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:32.035692image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
0.09988
99.9%
1.012
 
0.1%

Most occurring characters

ValueCountFrequency (%)
019988
66.6%
.10000
33.3%
112
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
66.7%
Other Punctuation10000
33.3%

Most frequent character per category

ValueCountFrequency (%)
019988
99.9%
112
 
0.1%
ValueCountFrequency (%)
.10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

ValueCountFrequency (%)
019988
66.6%
.10000
33.3%
112
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ValueCountFrequency (%)
019988
66.6%
.10000
33.3%
112
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0.0
9695 
1.0
 
305

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.09695
97.0%
1.0305
 
3.0%
2021-02-20T23:35:32.390224image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:32.525064image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
0.09695
97.0%
1.0305
 
3.0%

Most occurring characters

ValueCountFrequency (%)
019695
65.6%
.10000
33.3%
1305
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
66.7%
Other Punctuation10000
33.3%

Most frequent character per category

ValueCountFrequency (%)
019695
98.5%
1305
 
1.5%
ValueCountFrequency (%)
.10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

ValueCountFrequency (%)
019695
65.6%
.10000
33.3%
1305
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ValueCountFrequency (%)
019695
65.6%
.10000
33.3%
1305
 
1.0%

Gaming
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0.0
9685 
1.0
 
315

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.09685
96.9%
1.0315
 
3.1%
2021-02-20T23:35:32.806616image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:32.935611image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
0.09685
96.9%
1.0315
 
3.1%

Most occurring characters

ValueCountFrequency (%)
019685
65.6%
.10000
33.3%
1315
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
66.7%
Other Punctuation10000
33.3%

Most frequent character per category

ValueCountFrequency (%)
019685
98.4%
1315
 
1.6%
ValueCountFrequency (%)
.10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

ValueCountFrequency (%)
019685
65.6%
.10000
33.3%
1315
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ValueCountFrequency (%)
019685
65.6%
.10000
33.3%
1315
 
1.1%

ivy_league
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
9273 
1
 
727

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
09273
92.7%
1727
 
7.3%
2021-02-20T23:35:33.179756image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:33.272597image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
09273
92.7%
1727
 
7.3%

Most occurring characters

ValueCountFrequency (%)
09273
92.7%
1727
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10000
100.0%

Most frequent character per category

ValueCountFrequency (%)
09273
92.7%
1727
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
Common10000
100.0%

Most frequent character per script

ValueCountFrequency (%)
09273
92.7%
1727
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII10000
100.0%

Most frequent character per block

ValueCountFrequency (%)
09273
92.7%
1727
 
7.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
9998 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
09998
> 99.9%
12
 
< 0.1%
2021-02-20T23:35:33.514996image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:35:33.597177image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
ValueCountFrequency (%)
09998
> 99.9%
12
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
09998
> 99.9%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10000
100.0%

Most frequent character per category

ValueCountFrequency (%)
09998
> 99.9%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common10000
100.0%

Most frequent character per script

ValueCountFrequency (%)
09998
> 99.9%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10000
100.0%

Most frequent character per block

ValueCountFrequency (%)
09998
> 99.9%
12
 
< 0.1%

Interactions

2021-02-20T23:34:58.674683image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-02-20T23:34:58.898173image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-02-20T23:34:59.091804image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-02-20T23:34:59.241781image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-02-20T23:34:59.386018image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-02-20T23:34:59.601123image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-02-20T23:34:59.842861image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-02-20T23:35:00.068862image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-02-20T23:35:00.329468image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-02-20T23:35:00.571474image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-02-20T23:35:00.797035image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-02-20T23:35:00.982971image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-02-20T23:35:01.186567image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-02-20T23:35:01.373544image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-02-20T23:35:01.556416image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-02-20T23:35:01.814756image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-02-20T23:35:02.027809image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-02-20T23:35:02.239070image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-02-20T23:35:02.409389image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2021-02-20T23:35:02.615224image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Correlations

2021-02-20T23:35:33.798216image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-20T23:35:35.721575image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-20T23:35:37.924855image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-20T23:35:39.918436image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-02-20T23:35:41.703210image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-02-20T23:35:03.286245image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-20T23:35:08.072086image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-02-20T23:35:09.490859image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-02-20T23:35:09.784502image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Unnamed: 0country_coderegioncitystatustotal_funding_usdfounded_on_yearfounded_on_monthfounded_on_dayOtherScience and EngineeringInformation TechnologyMedia and EntertainmentSales and MarketingContent and PublishingNatural ResourcesData and AnalyticsFood and BeverageDesignConsumer GoodsCommunity and LifestyleAdministrative ServicesTransportationPaymentsSustainabilityProfessional ServicesEnergyMessaging and TelecommunicationsVideoAdvertisingNavigation and MappingFinancial ServicesMusic and AudioLending and InvestmentsHardwareAppsTravel and TourismBiotechnologyCommerce and ShoppingReal EstatePlatformsPrivacy and SecurityInternet ServicesSoftwareHealth CareClothing and ApparelEducationGovernment and MilitarySportsMobileManufacturingArtificial IntelligenceEventsAgriculture and FarmingConsumer ElectronicsGamingivy_leagueparticapated_event_first_year
00USANew YorkNew Yorkacquired39750000.02005.06.01.00.00.00.01.01.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.000
11USANew YorkNew Yorkacquired49000000.02004.010.011.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.010
22USACaliforniaPalo Altoclosed800000.02005.011.01.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.01.01.00.00.00.00.00.00.00.00.00.00.00.00.000
33USACaliforniaWest Hollywoodacquired15000000.02006.06.01.01.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000
44USACaliforniaCulver CityacquiredNaN2006.01.01.00.00.00.01.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.01.00.00.00.00.00.00.000
55USACaliforniaSan Franciscoclosed18500000.02001.01.01.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.010
66USANew YorkNew Yorkclosed32100000.01996.011.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.000
77USACaliforniaSan Franciscooperating105750000.02007.03.01.01.00.00.01.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.01.00.00.00.00.00.00.00.00.00.00.00.00.010
88USACaliforniaSan Diegoacquired75663277.02006.01.01.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.01.00.00.00.00.00.00.000
99USACaliforniaPalo Altoacquired44150000.02005.01.01.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.000

Last rows

Unnamed: 0country_coderegioncitystatustotal_funding_usdfounded_on_yearfounded_on_monthfounded_on_dayOtherScience and EngineeringInformation TechnologyMedia and EntertainmentSales and MarketingContent and PublishingNatural ResourcesData and AnalyticsFood and BeverageDesignConsumer GoodsCommunity and LifestyleAdministrative ServicesTransportationPaymentsSustainabilityProfessional ServicesEnergyMessaging and TelecommunicationsVideoAdvertisingNavigation and MappingFinancial ServicesMusic and AudioLending and InvestmentsHardwareAppsTravel and TourismBiotechnologyCommerce and ShoppingReal EstatePlatformsPrivacy and SecurityInternet ServicesSoftwareHealth CareClothing and ApparelEducationGovernment and MilitarySportsMobileManufacturingArtificial IntelligenceEventsAgriculture and FarmingConsumer ElectronicsGamingivy_leagueparticapated_event_first_year
99909990USAGeorgiaAtlantaoperatingNaN2001.01.01.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.000
99919991USACaliforniaLos AngelesoperatingNaN2001.02.020.00.00.00.01.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000
99929992USANew YorkNew Yorkipo11500000.02005.07.019.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.01.01.00.00.00.00.00.01.00.00.00.00.00.00.010
99939993NaNNaNNaNoperatingNaN2006.02.01.00.00.00.01.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.000
99949994FRAAquitaineAubinacquiredNaN2006.011.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.01.00.01.01.00.00.00.00.00.00.00.00.00.00.00.00.000
99959995NaNNaNNaNoperatingNaN2008.012.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.000
99969996USAIllinoisGlencoeacquired70000000.01995.012.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.01.00.00.00.00.00.01.00.00.00.00.00.00.000
99979997USACaliforniaWest HollywoodoperatingNaN1970.01.01.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.01.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.000
99989998CANOntarioMississaugaoperatingNaN1997.01.01.01.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000
99999999ESTTartumaaTartuoperatingNaN2008.08.05.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.000